Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport

• URL: http://arxiv.org/abs/2604.12663v1
• Date: Tue, 14 Apr 2026 12:31:26 GMT
• Title: Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport
• Abstract summary: We introduce Human-centric Topic Modeling, emphHuman-TM, a novel task formulation that integrates a human-provided goal directly into the topic modeling process.<n>We show that GCTM-OT outperforms state-of-the-art baselines in topic coherence and diversity while significantly improving alignment with human-provided goals.
• Score: 35.500865939660564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing topic modeling methods, from LDA to recent neural and LLM-based approaches, which focus mainly on statistical coherence, often produce redundant or off-target topics that miss the user's underlying intent. We introduce Human-centric Topic Modeling, \emph{Human-TM}), a novel task formulation that integrates a human-provided goal directly into the topic modeling process to produce interpretable, diverse and goal-oriented topics. To tackle this challenge, we propose the \textbf{G}oal-prompted \textbf{C}ontrastive \textbf{T}opic \textbf{M}odel with \textbf{O}ptimal \textbf{T}ransport (GCTM-OT), which first uses LLM-based prompting to extract goal candidates from documents, then incorporates these into semantic-aware contrastive learning via optimal transport for topic discovery. Experimental results on three public subreddit datasets show that GCTM-OT outperforms state-of-the-art baselines in topic coherence and diversity while significantly improving alignment with human-provided goals, paving the way for more human-centric topic discovery systems.

Related papers

• Human-Centric Intelligence in the Era of Foundation Models: A Survey [159.29291047252138]
  Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling.<n>Recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear.<n>To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonomy.
  arXiv  Detail & Related papers  (2026-08-18T08:12:40Z)
• Zero-Shot Human Mobility Forecasting via Large Language Model with Hierarchical Reasoning [27.096256356447117]
  ZHMF is a framework for zero-shot human mobility forecasting.<n>It combines a semantic enhanced retrieval and reflection mechanism with a hierarchical language model based reasoning system.
  arXiv  Detail & Related papers  (2025-09-20T08:46:38Z)
• Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning [23.816433328623397]
  Overemphasizing likelihood without incorporating topic regularization can lead to an overly expansive latent space for topic modeling.<n>We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability.<n>Our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs.
  arXiv  Detail & Related papers  (2024-12-23T07:07:06Z)
• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models [46.09562860220433]
  We introduce GazeReward, a novel framework that integrates implicit feedback -- and specifically eye-tracking (ET) data -- into the Reward Model (RM)<n>Our approach significantly improves the accuracy of the RM on established human preference datasets.
  arXiv  Detail & Related papers  (2024-10-02T13:24:56Z)
• Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification [49.41632476658246]
  We discuss the extension of DFKD to Vision-Language Foundation Models without access to the billion-level image-text datasets. The objective is to customize a student model for distribution-agnostic downstream tasks with given category concepts. We propose three novel Prompt Diversification methods to encourage image synthesis with diverse styles.
  arXiv  Detail & Related papers  (2024-07-21T13:26:30Z)
• A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling [10.589126787499973]
  Existing topic models often struggle to accurately capture the underlying semantic patterns of short texts.<n>This paper introduces a novel model-agnostic mechanism, termed Topic Refinement.<n>We show that Topic Refinement boosts the topic quality and improves the performance in topic-related text classification tasks.
  arXiv  Detail & Related papers  (2024-03-26T13:50:34Z)
• Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling [0.9095496510579351]
  We investigate the untapped potential of large language models (LLMs) as an alternative for uncovering the underlying topics within extensive text corpora. Our findings indicate that LLMs with appropriate prompts can stand out as a viable alternative, capable of generating relevant topic titles and adhering to human guidelines to refine and merge topics.
  arXiv  Detail & Related papers  (2024-03-24T17:39:51Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Knowledge-Aware Bayesian Deep Topic Model [50.58975785318575]
  We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Our proposed model efficiently integrates the prior knowledge and improves both hierarchical topic discovery and document representation.
  arXiv  Detail & Related papers  (2022-09-20T09:16:05Z)
• Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations [35.74225306947918]
  We propose a joint latent space learning and clustering framework built upon PLM embeddings. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery.
  arXiv  Detail & Related papers  (2022-02-09T17:26:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.